Missing Values Handling for Machine Learning Portfolios
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- Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
- Lewellen, Jonathan, 2015. "The Cross-section of Expected Stock Returns," Critical Finance Review, now publishers, vol. 4(1), pages 1-44, June.
- Lewandowski, Daniel & Kurowicka, Dorota & Joe, Harry, 2009. "Generating random correlation matrices based on vines and extended onion method," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1989-2001, October.
- Andrew Y. Chen & Tom Zimmermann, 2022.
"Open Source Cross-Sectional Asset Pricing,"
Critical Finance Review, now publishers, vol. 11(2), pages 207-264, May.
- Chen, Andrew Y. & Zimmermann, Tom, 2020. "Open source cross-sectional asset pricing," CFR Working Papers 20-04, University of Cologne, Centre for Financial Research (CFR).
- Andrew Y. Chen & Tom Zimmermann, 2021. "Open Source Cross-Sectional Asset Pricing," Finance and Economics Discussion Series 2021-037, Board of Governors of the Federal Reserve System (U.S.).
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
- Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020.
"Shrinking the cross-section,"
Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
- Serhiy Kozak & Stefan Nagel & Shrihari Santosh, 2017. "Shrinking the Cross Section," NBER Working Papers 24070, National Bureau of Economic Research, Inc.
- Nagel, Stefan & Santosh, Shrihari & Kozak, Serhiy, 2017. "Shrinking the Cross Section," CEPR Discussion Papers 12463, C.E.P.R. Discussion Papers.
- R. David Mclean & Jeffrey Pontiff, 2016. "Does Academic Research Destroy Stock Return Predictability?," Journal of Finance, American Finance Association, vol. 71(1), pages 5-32, February.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020.
"Empirical Asset Pricing via Machine Learning,"
The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
- Shihao Gu & Bryan T. Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," Swiss Finance Institute Research Paper Series 18-71, Swiss Finance Institute.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, National Bureau of Economic Research, Inc.
- Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
- Martin Lettau & Markus Pelger & Stijn Van Nieuwerburgh, 2020.
"Factors That Fit the Time Series and Cross-Section of Stock Returns,"
The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2274-2325.
- Lettau, Martin & Pelger, Markus, 2018. "Factors that Fit the Time Series and Cross-Section of Stock Returns," CEPR Discussion Papers 13049, C.E.P.R. Discussion Papers.
- Martin Lettau & Markus Pelger, 2018. "Factors that Fit the Time Series and Cross-Section of Stock Returns," NBER Working Papers 24858, National Bureau of Economic Research, Inc.
- Joachim Freyberger & Andreas Neuhierl & Michael Weber, 2020.
"Dissecting Characteristics Nonparametrically,"
The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2326-2377.
- Joachim Freyberger & Andreas Neuhierl & Michael Weber, 2017. "Dissecting Characteristics Nonparametrically," NBER Working Papers 23227, National Bureau of Economic Research, Inc.
- Joachim Freyberger & Andreas Neuhierl & Michael Weber & Michael Weber, 2018. "Dissecting Characteristics Nonparametrically," CESifo Working Paper Series 7187, CESifo.
- Joachim Freyberger & Andreas Neuhierl & Michael Weber & Michael Weber, 2017. "Dissecting Characteristics Nonparametrically," CESifo Working Paper Series 6391, CESifo.
- Titman, Sheridan & Wei, K. C. John & Xie, Feixue, 2004.
"Capital Investments and Stock Returns,"
Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 39(4), pages 677-700, December.
- Sheridan Titman & K.C. John Wei & Feixue Xie, 2003. "Capital Investments and Stock Returns," NBER Working Papers 9951, National Bureau of Economic Research, Inc.
- Joachim Freyberger & Andreas Neuhierl & Michael Weber & Andrew KarolyiEditor, 2020.
"Dissecting Characteristics Nonparametrically,"
Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2326-2377.
- Joachim Freyberger & Andreas Neuhierl & Michael Weber & Andrew KarolyiEditor, 2020. "Dissecting Characteristics Nonparametrically," Review of Finance, European Finance Association, vol. 33(5), pages 2326-2377.
- Joachim Freyberger & Andreas Neuhierl & Michael Weber, 2017. "Dissecting Characteristics Nonparametrically," NBER Working Papers 23227, National Bureau of Economic Research, Inc.
- Joachim Freyberger & Andreas Neuhierl & Michael Weber & Michael Weber, 2018. "Dissecting Characteristics Nonparametrically," CESifo Working Paper Series 7187, CESifo.
- Joachim Freyberger & Andreas Neuhierl & Michael Weber & Michael Weber, 2017. "Dissecting Characteristics Nonparametrically," CESifo Working Paper Series 6391, CESifo.
- Jeremiah Green & John R. M. Hand & X. Frank Zhang, 2017. "The Characteristics that Provide Independent Information about Average U.S. Monthly Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 30(12), pages 4389-4436.
- Martin Lettau & Markus Pelger, 2020. "Factors That Fit the Time Series and Cross-Section of Stock Returns," Review of Finance, European Finance Association, vol. 33(5), pages 2274-2325.
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This paper has been announced in the following NEP Reports:- NEP-ECM-2022-09-05 (Econometrics)
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